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Public and Private Policy Change: Pension Reform in Four Countries

2007· article· en· W2037324592 on OpenAlexaffabout
Daniel Béland, Toshimitsu Shinkawa

Bibliographic record

VenuePolicy Studies Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRestructuringPensionLegislatureInstitutional changePrivate pensionPublic policyPolitical scienceEconomicsEconomic policyEconomic growthPublic administrationLaw

Abstract

fetched live from OpenAlex

This article offers a comparative, qualitative analysis of the changing nature of—and relationship between—public and private old age pensions in the United States, Canada, Britain, and Japan. Stressing the impact of institutional legacies on policy change, the article explains why these countries have taken contrasting paths toward the restructuring of public and private pension policies. The study finds that the four countries fall into two distinct clusters. On the one hand are Canada and the United States, which have essentially witnessed policy drift toward a greater reliance on private savings. On the other hand are Britain and Japan, which have reshaped their pension systems largely through legislative revision. The last section explains the differences between and within these two country clusters. The article concludes that institutional forces explain the distinctive policy patterns between the two country clusters but that it is necessary to bring in other factors (i.e., demographic aging, union density, and the role of ideas) to account for differences within each of these clusters.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.007
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.196
GPT teacher head0.432
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2007
Admission routes2
Has abstractyes

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